AI - Applications & Models - Page 5#
Accelerating Kimi-K2.5 on AMD Instinct™ MI300X: Optimizing Fused MoE with FlyDSL
Optimize Kimi-K2.5 on AMD MI300X using FlyDSL for fused MoE kernel acceleration. Achieve faster TTFT, TPOT, and throughput with our step-by-step optimization guide.
Edge-to-Cloud Robotics with AMD ROCm: From Data Collection to Real-Time Inference
This blog demonstrates a comprehensive Edge-to-Cloud robotics AI solution powered by the AMD ecosystem and the Hugging Face LeRobot framework.
hipBLASLt Online GEMM Tuning
Learn how to improve model performance with hipBLASLt online tuning merged into LLM framework
Utilizing AMD Instinct GPU Accelerators for Weather and Precipitation Forecasting with NeuralGCM
A showcase of how to run NeuralGCM, a hybrid GCM model, on AMD Instinct hardware, including an introduction, installation, inference, and plotting.
Getting Started with ComfyUI on AMD Radeon™ RX 9000 Series GPUs
Learn how to set up and optimize ComfyUI on AMD Radeon RX 9000 GPUs with ROCm 7.1 — solve common issues and start generating.
HPC Coding Agent - Part 3: MCP Tool for Profiling
Build an AI agent specialized in optimizing HPC workloads by connecting a Cline agent to expert-level AMD profiling tools via a custom MCP server.
Fine-Tuning AI Surrogate Models for Physics Simulations with Walrus on AMD Instinct GPU Accelerators
A showcase of fine-tuning the foundational physics simulation model Walrus on a new physics dataset using AMD Instinct hardware.
Ensemble High-Resolution Weather Forecasting on AMD Instinct GPU Accelerators
A discussion on ensembling in weather forecasting, and a guide on how to run forecasting ensembles on AMD GPUs.
HPC Coding Agent - Part 2: An MCP Tool for Code Optimization with OpenEvolve
Learn how to use OpenEvolve as an MCP tool with an AI agent for agentic code optimization
Streamlining Recommendation Model Training on AMD Instinct™ GPUs
Explore how the ROCm training docker can be used for recommendation model training on Instinct GPUs, along with a guide on configuring the workload.
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm 7 Support for Efficient ML Workflows
Ray with ROCm helps you scale AI applications for training and inference workloads on AMD GPUs.
PyTorch Offline Tuning with TunableOp
Learn how to accelerate PyTorch workloads with TunableOp offline tuning—record, tune separately, and deploy faster inference.